Industrial Engineering

Faculty

Ying Lin
Ying Lin, Ph.D.
Assistant Professor
Office Location: Room E211
Phone: 713-743-6674   |   Fax: 713-743-4190
Email: ylin53 [at] central [dot] uh [dot] edu

Education: 

Ph.D., Industrial and Systems Engineering (ISE), University of Washington, 2017
M.S., Industrial and Management Systems Engineering (IMSE), University of South Florida, 2014
B.S., Statistics, University of Science and Technology of China, P. R. China, 2012

Research Interests: 

Data analytics to facilitate effective use of information technology, quality improvement in complex system, and better decision-making in clinical practice. Methodologies: Statistical modeling; Optimization; Data mining; Operations research. Application: Complex system modeling, monitoring and management; Disease diagnostics and prognostics; Adaptive monitoring; Cost-effectiveness analysis; Medical decision making.

Selected Publications

  • Lin, Y., Huang, S., Simon, G.E., and Liu, S., “Analysis of Depression Trajectory Patterns using Collaborative Learning”, Mathematical Biosciences, in press

    , 2017.
  • Li, M., Lin, Y., Huang, S., and Crossland, C., “The Use of Sparse Inverse Covariance Estimation for Relationship Detection and Hypothesis Generation in Strategic Management”, Strategic Management Journal, 37: 86-97

    , 2016.
  • Lin, Y., Qian, X., Krischer, J., Vehik, K., Lee, H.S. and Huang, S, “A Rule-Based Prognostic Model for Type 1 Diabetes by Identifying and Synthesizing Baseline Profile Patterns”, PLOS one, 9(6): e91095

    , 2014.

Recent Presentations

  • “Patient-specific Depression Monitoring by Selective Sensing”, INFORMS Annual Conference, Nashville, TN

    , 2016.
  • “Adaptive Monitoring of Depression Treatment Population: A Data-driven Approach”, INFORMS Annual Conference, Nashville, TN

    , 2016.
  • “A Longitudinal Pattern based Prognostic Model for Depression Monitoring via Rulebased Method”, SMDM 38th Annual Meeting, Vancouver, CA

    , 2016.
  • “Adaptive Monitoring of Depression Patient Population: A Selective Sensing Approach”, SMDM 38th Annual Meeting, Vancouver, CA

    , 2016.
  • “A Longitudinal Pattern based Prognostic Model for Depression Monitoring via Rulebased Method”, Group Health Research Institute, Seattle, WA

    , 2016.
  • “Large-Scale Personalized Health Surveillance by Collaborative Modeling and Selective Sensing”, INFORMS Annual Conference, Philadelphia, NJ

    , 2015.
  • “Analysis of Electronic Health Record based Depression T rajectory and Monitoring”, INFORMS Annual Conference, Philadelphia, NJ

    , 2015.
  • “Collaborative Alerts Ranking for Intrusion Detection”, NEC Laboratories America, Princeton, NJ

    , 2015.
  • “Trajectory Modeling via Collaborative Learning Approach”, NEC Laboratories America, Princeton, NJ

    , 2015.
  • “Cognitive Degradation Modeling for Alzheimer’s Disease via A Collaborative Degradation Modeling Approach,” SDM 2015, Vancouver, CA

    , 2015.
  • “Domain-Knowledge Driven Cognitive Degradation Modeling for Alzheimer’s Disease,” INFORMS Annual Conference, San Francisco, CA

    , 2014.

Conference Publications

  • Lin, Y., Liu, S., and Huang, S., “A Longitudinal Pattern Based Prognostic Model for Depression Monitoring via Rule-based Method”, in 38th Annual Meeting of the Society for Medical Decision Making (SMDM), Oct 23 - Oct 24, 2016, Vancouver, CA. (Abstract)

    , 2016.
  • Lin, Y., Liu, S., and Huang, S., “Adaptive Monitoring of Depression Patient Population: A Selective Sensing Approach”, in 38th Annual Meeting of the Society for Medical Decision Making (SMDM), Oct 23 - Oct 24, 2016, Vancouver, CA. (Abstract)

    , 2016.
  • Lin, Y., Huang, S., and Liu, S., “Analysis of Depression Trajectory Patterns Using Collaborative Learning”, in 37th Annual Meeting of the Society for Medical Decision Making (SMDM), Oct 18 - Oct 21, 2015, St. Louis, MO. (Abstract: invited for oral presentation)

    , 2015.
  • Lin, Y., Liu, K., Byon, E., Qian, X., and Huang, S., “Domain-knowledge Driven Cognitive Degradation Modeling for Alzheimer’s Disease”, in SIAM International Conference on Data Mining 2015 (SDM 2015), Apr 30 - May 2, 2015, Vancouver, CA

    , 2015.